Method and apparatus for return on investment impact reporting

ABSTRACT

The method, apparatus and computer program product described herein is configured to train and deploy a predictive model that is configured to generate a predicted ROI value for a provider with respect to a current promotion or a future promotion. An example embodiment may comprise receiving input indicative of one or more attributes of a provider or a promotion. The example embodiment may further comprise generating at least one of a predicted return on investment (ROI) value or a predicted ROI component value based at least in part on the one or more attributes of the provider or the promotion and a ROI prediction model. The method may further still comprise generating a merchant impact report including the at least one of the predicted ROI value or the predicted ROI component value for the promotion.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims the benefit of U.S. ProvisionalPatent Application No. 61/682,762, filed Aug. 13, 2012, the entirecontents of which are incorporated herein by reference.

This application is also related to application Ser. No. 13/841,347entitled UNIFIED PAYMENT AND RETURN ON INVESTMENT SYSTEM filed Mar. 15,2013, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present description relates to an effective and efficient way tobetter communicate a return on investment value to a merchant of runninga transaction, such as a promotion, and determine a transactionstructure that is conducive to the goals of the merchant, the customer,the promotion system, or any combination thereof. This description morespecifically relates to how to provide real-time ROI calculations that amerchant and sales representative may collaboratively and simultaneouslywork on to identify a transaction structure for the merchant to select.

BACKGROUND

Promotion and marketing services often work with merchants to identifypromotions to offer to potential customers. By developing appropriatepromotions, merchants may increase profit, a promotion and marketingservice may generate revenue, and customers may find new and interestinggoods and/or services at discount prices.

After offering a promotion on behalf of a merchant, the promotion andmarketing service may distribute revenue to the merchant for promotionssold to customers. However, when a customer seeks a refund of apromotion, the merchant may not be owed any money for the refundedpromotion. In order to account for potential refunds, the promotion andmarketing service may pay merchants less than what is fully owed. Ifthere are refunds, the promotions system reduces the outstanding amountdue to the merchant by the merchant's share of the refunded revenue (or,in the case of a merchant that has already been paid the entire amountdue or that has an outstanding amount due to the promotion and marketingservice, the reduction is carried over into another session: the nextpromotion).

Applicant has identified deficiencies and problems associated with theuse of these systems. As described in detail below, Applicant has solvedthese identified problems by developing a solution that is embodied bythe present invention.

BRIEF SUMMARY

In some example embodiments, a promotions system may be configured togenerate a real-time ROI as output for one or more promotions. In someexamples, the ROI may be operable to optimize the selection ofpromotions during negotiation between merchants and a promotion andmarketing service. The system includes a communications interfaceconfigured to receive inputs indicative of one or more attributes of thepromotion, an upsell amount exceeding a value of the promotion, and oneor more indicators of repeat business in response to the promotion, anda processor in communication with the interface.

The system provides, in some examples, a better way to communicate thevalue to merchants of running a promotion and arrive at a deal structurethat works for both the merchant and the promotion and marketingservice. The system avoids sub-optimal deals for merchants that resultfrom the merchant's lack of understanding. The real-time ROI calculationtool allows merchants and sales representatives to collaboratively workat the same time with common visual representation. A salesrepresentative may dynamically lock or unlock certain fields frommerchant manipulation, may allow off-line merchant manipulation of thetool, and may enable the use of predictive wizards,analytics/demographic information, and similar promotions to help arriveat a deal structure. The real-time ROI calculation tool also, forexample, provides a similar view on the sales representative's side asthe merchant's side, so that changes made on either side are immediatelyreflected by both the sales representative's side and the merchant'sside. The sales representative may decide to lock certain fields toprevent a merchant from editing. The system also notifies the salesrepresentative when a merchant opens and edits the ROI criteria.

Other systems, methods, and features will be, or will become, apparentto one with skill in the art upon examination of the following figuresand detailed description. It is intended that all such additionalsystems, methods, features and be included within this description, bewithin the scope of the disclosure, and be protected by the followingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The system, method and product may be better understood with referenceto the following drawings and description. Non-limiting andnon-exhaustive descriptions are described with reference to thefollowing drawings. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating principles. Inthe figures, like referenced numerals may refer to like parts throughoutthe different figures unless otherwise specified.

FIG. 1 shows an example graphical consumer interface for merchants inaccordance with example embodiments;

FIG. 2 shows an example revenue and profit impact control interface forsales representatives in accordance with example embodiments;

FIG. 2a shows merchant share of revenue calculation;

FIG. 2b shows merchant share of revenue and revenue from additionalspend calculation;

FIG. 2c shows merchant share of revenue, revenue from additional spendand repeat customer revenue calculation;

FIG. 2d shows revenue and costs according to the merchant share,additional spend and repeat customer activity;

FIG. 3 shows a display interface for a mobile device;

FIG. 3a shows another display interface for a mobile device;

FIG. 3b shows one other display interface for a mobile device;

FIG. 3c illustrates an example graphical consumer interface for amerchant, showing demographic information relating to a promotion;

FIG. 3d illustrates an example graphical consumer interface for amerchant, showing customer survey information relating to a promotion;

FIG. 3e illustrates an example graphical consumer interface for amerchant, showing revenue, cost, and profit information regarding apromotion;

FIG. 3f illustrates an example graphical consumer interface that amerchant may use to update the marginal cost of a promotion;

FIG. 4 shows a configuration of the ROI system;

FIG. 5 shows a diagram of logic of how the merchant revenue iscalculated;

FIG. 6 shows a flow diagram of logic of how the merchant cost iscalculated;

FIG. 7 shows a flow diagram of logic of how the merchant profit iscalculated;

FIG. 7a shows a flow diagram of example operations used by the paymentsystem to schedule and distribute funds to a merchant; and

FIG. 8 shows a configuration of the unified payment and ROI system.

DETAILED DESCRIPTION

The principles described herein may be embodied in many different forms.Not all of the depicted components may be required, however, and someimplementations may include additional, different, or fewer components.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional, different or fewer components may be provided.

Definitions

As used herein, a promotion may include, but is not limited to, any typeof offered, presented or otherwise indicated reward, discount, coupon,credit, deal, incentive, discount, media or the like that is indicativeof a promotional value or the like that upon purchase or acceptanceresults in the issuance of an instrument that may be used toward atleast a portion of the purchase of particular goods, services and/orexperiences defined by the promotion. An example promotion, using arunning company as the example merchant, is $25 for $50 toward runningshoes. In some examples, the promotion defines an accepted value (e.g.,a cost to purchase the promotion), a promotional value (e.g., the valueof the resultant instrument beyond the accepted value), a residual value(e.g., the value upon return or upon expiry of one or more redemptionparameters), one or more redemptions parameters and/or the like. Forexample, using the running company promotion as an example, the acceptedvalue is $25 and the promotional value is $50. In this example, theresidual value may be equal to the accepted value.

As used herein, a promotion and marketing service may include a servicethat is accessible via one or more computing devices and is operable toprovide example promotion and/or marketing services on behalf of one ormore providers that are offering one or more instruments that areredeemable for goods, services, experiences and/or the like. Thepromotion and marketing service is further configured to illustrate orotherwise inform one or more consumers of the availability of one ormore instruments in the form of one or more impressions. In someexamples, the promotion and marketing service may also take the form ofa redemption authority, a payment processor, a rewards provider, anentity in a financial network, a promoter, an agent and/or the like. Assuch, the service is, in some example embodiments, configured to presentone or more promotions via one or more impressions, accept payments forpromotions from consumers, issue vouchers upon acceptance of an offer,participate in redemption, generate rewards, provide a point of saledevice or service, issue payments to providers and/or or otherwiseparticipate in the exchange of goods, services or experiences forcurrency, value and/or the like. The service may additionally processrefund requests received from consumers who have been issued vouchers.For example, using the aforementioned running company promotion, acustomer who has paid the service $25 for a voucher, may subsequentlyrequest a refund of the residual value of the promotion in conjunctionwith returning and/or otherwise invalidating the voucher. The promotionand marketing service may accordingly credit $25 to the customer andensure that the voucher is destroyed and/or otherwise invalidated.

As used herein, a voucher may include, but is not limited to, any typeof gift card, tender, electronic certificate, medium of exchange, or thelike that embodies the terms of the promotion from which the voucherresulted and may be used toward at least a portion of the purchase,acquisition, procurement, consumption or the like of goods, servicesand/or experiences. In some examples, the voucher may take the form oftender that has a given value that is exchangeable for goods, servicesand/or experiences and/or a reduction in a purchase price of aparticular good, service or experience. In some examples, the vouchermay have multiple values, such as accepted value, a promotional valueand/or a residual value. For example, using the aforementioned exampleof a running company, the promotional value may be received as anelectronic indication in a mobile application that shows $50 to spend atthe running company. In some examples, the accepted value of the voucheris defined by the value exchanged for the voucher. In some examples, thepromotional value is defined by the promotion from which the voucherresulted and is the value of the voucher beyond the accepted value. Insome examples, the residual value is the value after redemption, thevalue after the expiry or other violation of a redemption parameter, thereturn or exchange value of the voucher and/or the like.

As used herein, an impression may include a communication, a display, orother perceived indication, such as a flyer, print media, e-mail, textmessage, application alert, mobile applications, other type ofelectronic interface or distribution channel and/or the like, of one ormore promotions. For example, using the aforementioned running companyas the example provider, an impression may comprise an e-mailcommunication sent to consumers that indicates the availability of a $25for $50 toward running shoes promotion.

Overview

A merchant typically has several venues in which to offer the sale ofthe merchants' product or service. One such venue is a website, whichmay assist the sale of the product or service offered by the merchant.However, it may be difficult for the merchant to determine the impact tothe merchant's business of using the website. To assist in determiningthe impact, a Return On Investment (ROI) system may be used. The ROIsystem, illustrated in more detail in FIG. 4, may be a server-basedsystem configured to receive input from multiple sources, such as from amerchant computing device and a sales representative computing device,in order to determine the impact of using the website.

For example, each of the merchant computing device and the salesrepresentative computing device may access the server-based ROI systemin order to receive a revenue and profit impact (RPI) control interface100 (discussed in more detail in FIG. 1). The merchant, via the merchantcomputing device, and the sales representative for the website, via thesales representative computing device, may input different parametersrelevant to the impact of the website assisting in the transaction. Inturn, the ROI system is configured to receive the input from thedifferent parties, and push the RPI of the website transaction to themerchant computing device and the website-representative computingdevice. In this way, the merchant and the sales representative may bothcontribute to the determination of the impact of the website assistingin the transaction. Further, because one, some, or all of the parametersrelevant to the impact of the website assisting in the transaction arechangeable, the merchant and the sales representative may change variousparameters to iteratively determine the impact.

The ROI system may be integrated with different systems of the website.For example, the ROI system may communicate with a historical databaseillustrating historical data of previous transactions. The ROI systemmay access the historical database in order to populate one or moreparameters relevant to the impact of the website assisting in thetransaction. As another example, the ROI system may communicate with awebpage database, which may store data to generate webpages. Morespecifically, after the merchant and the website representative agree onthe terms of the transaction, the ROI system may access the webpagedatabase, generate a webpage using the webpage database and the agreedterms of the transaction, and present the generated webpage to themerchant, via the merchant computing device, and to the websiterepresentative, via the website representative computing device. The ROIsystem may, in turn, receive input (such as changes) to the generatedwebsite from the merchant or the website representative.

FIG. 1 shows a Revenue and Profit Impact (RPI) control interface 100generated by the ROI system and also referred to as a return oninvestment (ROI) calculator for merchants. The system may generate amerchant view (as shown in FIG. 1) and a sales representative view (asshown in FIG. 2). As discussed above, the RPI control interface 100illustrates the revenue and profit impact of using the website to assistin the merchant transaction.

As one example, the transaction may comprise a promotion facilitated bythe website. In particular, the transaction may comprise a promotion inthe form of a Groupon® voucher, example terms of which are illustratedin FIG. 1, from the perspective of the merchant. The terms shown in FIG.1 are for illustration only, and other types of transactions arecontemplated.

The RPI control interface 100 illustrates one or more parameters relatedto the promotions. For example, the RPI control interface 100illustrates “Your Groupon Check” 102 selectable criteria, including theaverage check amount for two individuals 104, Groupon (voucher) Price106, (Avg) Groupon value 108, customers per voucher 110, unit cap 112and merchant share 114. The RPI control interface 100 provides“Additional Spend” selectable criteria 116 that includes upsell 118,“Repeat Customers” selectable criteria 120 that includes return ratepercentage 122 and return visits per year 124, and “Merchant Costs” 126selectable criteria that includes food cost percentage 128. The fieldsillustrated in FIG. 1 are merely for illustration purposes.

The various fields in FIG. 1 may be fixed or may be changeable via inputfrom the merchant or sales representative. Initial entries in thevarious fields may be based on a past promotion offered by the merchant,or may be individual preselected by the sales representative. Theinitial entries may instead be randomly generated or may be generatedbased on entries that are historically common for similarly situatedmerchants (e.g., merchants having a similar merchant type, size,service, location etc.). In yet another alternative, the fields may beinitially left blank initially, and are only filled in during an actualnegotiation between the merchant and the sales representative. As shownin FIG. 1, for example, various fields are in gray, indicating thatthose fields have been locked by the sales representative and are notchangeable by merchant input. In particular, fields 106, 108, 114 areillustrated in gray. By contrast, fields 104, 110, 112, 118, 122, 124,and 128, which are not grayed out, may be changed by the merchant. Inthis way, merchant input may be used to change various fields to betterillustrate the potential effects of offering the promotion. For example,the return rate 122 may initially be populated with a predeterminedpercentage based on historical analysis of previous promotions.Thereafter, the return rate 122 may be changed via consumer input. Inthis way, the RPI control interface 100 may be used iteratively todetermine the potential effect of offering the promotion program.

The average check for two 104 identifies the average amount a singleparty of customers spends at a merchant's business in a single visit.The Groupon Price 106 identifies the amount at which a Groupon customerwill purchase the merchant's offer. As one example, Groupon may offer atleast a 50% discount of the average retail value to attract newcustomers to the merchant's business. The (Avg) Groupon Value 108identifies the promotional amount a customer receives toward thepurchase of specified goods or services at the merchant's. Forpromotions related to experiences, this is the amount a customertypically spends for items included in the experience. TheCustomers/Groupon 110 identifies the average size of a party for asingle visit (e.g., 3 out of 4 merchants report that Groupon customersbring friends when redeeming their Groupon voucher). For experiences,this represents the number of customers who will participate in theexperience. The Unit Cap 112 identifies the number of units that Grouponcan sell over the duration of a promotion campaign. Based on previoushistory, it is estimated that approximately 20% of units will beredeemed in each of the first and last months of the campaign, with acontinuous stream of redemptions in the intervening months. The MerchantShare 114 identifies the revenue that the merchant may expect to receivefrom a Groupon. The merchant receives payment shortly after themerchant's offer is purchased, so that the payment can be used to helppay down costs associated with producing the merchant's offer.

The Merchant Share 114, which may otherwise be known as the providermargin, may be manually entered or may be automatically calculated bythe ROI system based on one or more of the following values: ahistorical information margin that compares reviews of the merchant toreviews of similar merchants; a provider profile margin, taking intoaccount a merchant quality score; a promotion structure margin, whichtakes into account the size of the discount, the Unit Cap 112,historical margins, and margins for similar discounts and units; or apositive ROI margin, which identifies a minimum margin that provides themerchant with a positive ROI. Such values may be used alone oraggregated through a linear combination or other similar aggregationmethod. Further explanation of such values and associated calculationsis provided by U.S. Provisional Patent Application 61/770,174, titled“Smart Pricing,” and U.S. patent application Ser. No. 13/832,804, titled“Smart Pricing,” which are each respectively incorporated by referencein their entireties.

The “Additional Spend” 116 selectable criteria include upsell amount118, which identifies the amount a customer spends on goods or servicesthat exceeds the value of the Groupon voucher. Based on analysis ofprevious Groupon voucher redemptions, it is estimated that customers onaverage spend 55% more than the value of their Groupon voucher.

The “Repeat Customers” 120 selectable criteria includes Return Rate %122 that identifies the percentage of new customers the merchanttypically attracts back to the merchant's business. Based on analysis ofrepeat customers, the system estimates that the return rate % 122 forcustomers whose arrival is prompted by purchasing a Groupon is similarto that of other new customers who come in.

The “Merchant Costs” 126 selectable criteria includes Food Cost % 128that identifies the incremental (variable) cost to produce the value ofthe Groupon voucher. With Groupon, this cost may be incurred when acustomer redeems his or her voucher. Average food and beverage costs mayrange from 28-35% of the purchase price.

The merchant's revenue (“Your Revenue”) 130 includes revenue from“Repeat Customer Revenue” 132, “Additional Spend Revenue” 134 and “YourGroupon Check” 136. As shown in FIG. 1, the ROI system calculates repeatcustomer revenue 132 using the following formula:

${{Repeat}\mspace{14mu} {Customer}\mspace{14mu} {Revenue}} = {{Unit}\mspace{14mu} {Cap} \times \frac{Customers}{Groupon} \times {Average}\mspace{14mu} {Check}\mspace{14mu} {for}\mspace{14mu} 2 \times ( {{Return}\mspace{14mu} {Rate}\mspace{14mu} \%} ) \times \frac{{Return}\mspace{14mu} {Visits}}{Year}}$

In this example, with a unit cap of 100, 2 customers per Groupon, anaverage check for two of $60, a return rate of 10%, and 2 return visitsper year, the repeat customer revenue 132 of this example is100×2×60×10%×2, or $2,400.

Further, the ROI system calculates additional spend revenue 134 usingthe following formula:

Additional Spend Revenue=Unit Cap×Average Upsell

In this example, with a unit cap of 100 and an average upsell of $20,the additional spend revenue 134 of this example is 100×20, or $2,000.

Finally, the ROI system calculates Your Groupon Check revenue 136 usingthe following formula:

Your Groupon Check=Unit Cap×Merchant Share

In this example, with a unit cap of 100 and a merchant share of 10, theYour Groupon Check revenue 136 is 100×10, or $1,000.

Accordingly, the merchant's revenue 148 in this example is$2,400+$2,000+$1,000=$5,400.

The merchant's cost (“Your Cost”) 138 includes Repeat Revenue Cost 140,Additional Spend Cost 142, and Check Cost 144. The ROI system calculatesthe costs by multiplying the corresponding revenue by Food Cost % 128.Accordingly, the repeat revenue cost 140 is the repeat customer revenue132 multiplied by the Food Cost 128, or $2,400×35%=$840. The additionalspend cost 142 is the additional spend revenue 132 multiplied by theFood Cost 128, or $2,000×35%=$700. Finally, the check cost 144 is theYour Groupon Check revenue 136 multiplied by the Average Groupon Value108 multiplied by the Food Cost 128, or 1,000×$40×35%=1,400.

Accordingly, the merchant's cost 150 in this example is$840+$700+$1,400=$2,940.

The ROI system calculates the merchant's profit (identified as “YourProfit” 146) as Your Revenue 148 (shown as S5,400) minus Your Cost 150(shown as $2,940), which equals Your Profit 152 (shown as $2,460).

The number of new customers 156 brought in by the promotion can becalculated by the ROI system as the unit cap 100 multiplied by thenumber of customers per Groupon, or 100×2=200. The investment percustomer 158 is the check cost 144 minus the Your Groupon Check cost 154divided by the number of new customers 156, or ($1,400−$1,000) ÷200=$2.

Finally, the Return On Investment (ROI), which comprises the revenuegenerated by each dollar spent on marketing using promotions, can beviewed as a ratio of the merchant's revenue 148 to the Your GrouponCheck cost 154. With a revenue of $5,400 and total spent of $1,000, theROI system in this example produces a ROI ratio 160 of $5,400: $1,000,or 5.4:1.

The ROI system may automatically update each of the above calculationsand graphical representations as values are entered in the criteriafields and/or when the consumer selects the “update calculation” 162.

FIG. 2 shows a revenue and profit impact control interface 200 generatedby the ROI system for sales representatives. The sales representativemay control whether an ROI criteria is selectable (editable) by themerchant from the merchant's view. The sales representative may use therevenue and profit impact control interface 200 to lead a dialogue withthe merchant to determine a mutually agreeable ROI for the merchant. TheROI criteria values may be selected (e.g., by the sales representative)and/or automatically selected by the system based on the merchant,merchant type or some other criteria. Reference deal structures may beused to prefill the values to pre-populate the return on investmentcalculations. For example, the sales representative may select a defaultset of ROI criteria from a repository of promotion criteria 202. Thevalues entered by the merchant and/or the sales representative areadjusted on the graphical display in real-time. In this regard,functions described herein as real-time need not actually occur withoutany delay at all, but may occur without perceivable delay, or in otherwords, in substantially near real-time. In one such embodiment, thegraphical representation may be updated using a third party service(e.g., www.pusher.com, which may perform updates with an average delayof 5 milliseconds) that is responsive to values entered by the merchantand/or the sales representative. The system provides a way to build alive graphical representation of a return on investment calculationcollaboratively by a merchant and sales representative.

The sales representative view includes consumer selectable icons (e.g.,204, 206, 208, 210) that may not be viewable or selectable by themerchant. For example, the sales representative view includes “lock”icons (e.g., 204, 206, 208) that can be toggled to lock or unlock aparameter. As another example, the data format of a field may bechanged. For example, the “merchant share” may be represented as apercentage of the total revenue or a dollar amount for the “merchantshare” data format, by toggling icon 210.

The system provides mouse over 212 views for each of the sub-componentsof “Your Revenue” 130 (e.g., “repeat customer” 214, “additional spend”216, “merchant share” 218), “Your Cost” 132, and “Your Profit” 134calculations that display the calculations used to calculate the amountsin each category (130, 138, 146).

FIG. 2a shows the merchant share 200 a of revenue calculation. Themerchant share 200 a of a revenue calculation may be calculated by theROI system using the inputs to the “Your Groupon Check” 102 criteria.

FIG. 2b shows the merchant share 200 b of revenue and also the revenuefrom the additional spend calculation. The merchant share of revenue andrevenue from Additional Spend 200 b calculation may be calculated by theROI system using the inputs to the “Your Groupon Check” 102 criteria and“Additional Spend” 116 criteria.

FIG. 2c shows the merchant share 200 c of revenue, revenue fromadditional spend, and the repeat customer revenue calculation. Themerchant share of revenue from additional spend and repeat customerrevenue calculation 200 c may be calculated by the ROI system using theinputs to the “Your Groupon Check” 102 criteria, “Additional Spend” 116criteria and “Repeat Customer” 120 criteria.

FIG. 2d shows revenue and costs 200 d according to the merchant share,additional spend and repeat customer criteria, as calculated by the ROIsystem.

FIG. 3 shows a mobile device display interface 300. The system maycommunicate the ROI interface in a way to accommodate the display of themobile device. The sales representative may communicate the ROIcalculator to a mobile device of a merchant and the ROI calculatoradapts to the viewing area of the mobile device being used to view theROI calculator. For example, the graph may be displayed in the centerarea of the display (e.g., using two columns instead of three columns).Depending on the capability of the mobile device display interface, theROI system may collapse the “your cost” and “your profit” columns (seeitem 302), and may arrange information (e.g., 154, 156, 158, 160) sothat the information is easily viewable (see item 304).

FIG. 3a shows another display interface 300 a for a mobile device suchas a tablet computing device. FIG. 3b shows one other display interface300 b for a mobile device such as a smart phone.

In some embodiments, the above-described features may be used to providemerchants with a rich source of relevant information about existingpromotions. In this regard, the ROI system may populate and present tomerchants a Merchant Impact Report, which enables the merchant toevaluate the performance of an existing promotion based on thepreviously described calculations, as well as additional data collectedby the ROI system.

FIG. 3c shows an impressions interface 306 generated by the ROI systemthat may be presented to a merchant in connection with a selectedpromotion. The impressions interface 306 displays the number ofimpressions 308 of the promotion that are sent to promotion andmarketing service subscribers. In this example, the impressions are sentto subscribers via email, although other delivery mechanisms (such asthose described previously) are contemplated. For instance, theimpressions may be distributed using a mobile device application orwebsite.

Some fraction of subscribers receiving impressions may subsequentlypurchase the promotion. Because promotions are purchased from thepromotion and marketing service, the promotion and marketing service isable to compile demographic information regarding the subscribers whohave purchased the promotion and present such demographic information tothe ROI system as attributes of the promotion. Thereafter, the ROIsystem can calculate, based on the attributes of the promotion, thegender, age, and zip code of the subscribers who have purchased thepromotion.

Using the impressions interface 306, the ROI system is able to displayto the merchant a gender representation 310 of the gender of thecustomers who have purchased the promotion. For instance, the genderrepresentation 310 may include a percentage of customers who havepurchased the promotion that are male and a percentage of the customerswho have purchased the promotion that are female.

Similarly, the impressions interface 306 may display to the merchant anage representation 312. The age representation 312 may include the agesof customers who have purchased the promotion. In one embodiment, theage representation 312 may include a histogram showing a number ofcustomers who have purchased the promotion in one or more age ranges.

Using the impressions interface 306, the ROI system is able to displayto the merchant a representation 314 of the zip codes of the customerswho have purchased the promotion. This representation may include ahistogram showing a number of customers who have purchased the promotionfor each zip code. The representation 314 may also include a map showingthe zip codes of the customers who have purchased the promotion. In oneembodiment, the map is a cluster map, which places a circle over eachzip code of a customer who has purchased the promotion, and varies thesize of the circle based on the number of customers in the zip code.

Although impressions interface 306 may display demographic informationregarding subscribers who have purchased the promotion, the impressionsinterface 306 may additionally or alternatively display demographicinformation regarding subscribers to whom impressions have beendelivered (i.e., subscribers who have been sent an email advertising thepromotion).

FIG. 3d shows a customers interface 316, generated by the ROI systemthat may be presented to a merchant in connection with a selectedpromotion. The interface 316 displays the number of customers who haveredeemed the promotion 318. In addition, the customers interface 306 maydisclose the percentage of purchased promotions that have been redeemed320. Moreover, the promotion and marketing service may request that thecustomers who have redeemed the promotion 318 complete surveys abouttheir experience with the merchant. Based on the survey results, thecustomers interface 316 may display additional information to themerchant.

For instance, based on the survey results, the ROI system may calculatean average rating of the merchant. The customers interface 316 may thendisplay the average merchant rating 322 (using, for instance, a numberline or other similar graphical format). In one embodiment, thecustomers interface 316 may additionally display a percentage ofcustomers who would recommend the merchant's business to a friend.

Similarly, based on the survey results, the ROI system may determine thenumber of customers who were new to the merchant at the time ofredeeming the promotion and the number of customers that had not visitedthe merchant for a predetermined amount of time (such as three months)prior to redeeming the promotion. Accordingly, in one embodiment, thecustomers interface 316 may additionally display a chart 324 indicatinga percentage of customers who were new to the merchant at the time ofredeeming the promotion, a percentage of customers that had not visitedthe merchant for a predetermined amount of time prior to redeeming thepromotion, and a remaining percentage of customers.

In one embodiment, the ROI system may calculate, based on customerrewards information, an estimated percentage of new customers that willreturn to the merchant within a predetermined amount of time ofredeeming a voucher. In one embodiment, this information may be based onmetrics, such as but not limited to the historical return rate ofexisting customers, tracked using customer rewards information. Inanother embodiment, it may be based on a metric that tracks the numberof customers that would receive an additional promotion for returning.In yet another embodiment, the estimated percentage of new customersthat will return is based on a metric generated from an evaluation ofthe past behavior of the new customers, as shown by the customer rewardsinformation. Accordingly, in this embodiment, the customers interface316 may additionally display the estimated percentage of new customersthat will return to the merchant within the predetermined amount of timeof redeeming a voucher 326 (using, for instance, a number line or othersimilar graphical format).

In some examples embodiments, there may not be a sufficient data togenerate a particular metric or a set of metrics for a particularmerchant, metrics may be unknown for a particular merchant, additionalmetrics may be required for a more specific or accurate ROI value and/orthe like. In such cases, a dataset that is generated based on historicalmetrics (historical data, survey results, historic ROI data and/or thelike) for all merchants may be used to train and/or test an ROIprediction model to approximate ROI and its components (e.g., estimaterevenue from consumers who returned to merchant after a first visit,estimate amount spent by Groupon customers over the promotion discount,amount paid to the merchant for total promotions sold). For example, theROI prediction model may be trained to classify a particular metricvalue as indicative of a positive ROI and/or may learn that thecombination of two metrics can be used as a predictor for number ofconsumers who will return to a merchant after a first visit.

In some example embodiments, the machine learning model may be used togenerate predictive algorithms for ROI and its components for a merchantthat is running a current promotion. The machine learning model may beoperable to input one or more metrics relating to the current merchantand the current promotion. Based on the similarity determined betweenthe one or more metrics, the current promotion and/or the merchant, theROI predictive model may generate one or more estimated metrics. Theestimated metrics are suggestive of probabilistic values when comparedto the trained model. In some example embodiments, the estimated metricsmay be used in calculations that provide an estimate of ROI and/or ROIcomponents. The ROI and the components thereof may therefore bepredicted and displayed, such as is shown in 148-152 of FIG. 3 e.

FIG. 3e shows a revenue interface 328, generated by the ROI system,which may be presented to a merchant in connection with a selectedpromotion. The revenue interface 328 displays the financial impact ofthe promotion. For instance, it may display the revenue 330 generated bythe promotion so far. Revenue 330 may comprise the merchant's revenue148 as discussed previously. In addition, the customers interface 306may display a breakdown of the merchant's financial information 332.This information includes the merchant's revenue 148, merchant's cost150, and merchant's profit 152, calculated by the ROI system aspreviously described. In one embodiment, this financial information 332may be displayed as a histogram.

In another embodiment, the merchant's financial information 332 may beconfigurable when the merchant uses an input device to select the costeditor 334. The merchant may edits its cost (e.g., projected, estimated,or actual) using the cost editor 334. For example, in the depictedembodiment, the merchant may edit its cost from 11% (as shown) to 13%upon receiving notice that its cost of materials for certain rawmaterials have risen.

Alternatively or additionally and in some embodiments, the ROI systemmay be configured to determine a positive ROI margin μ_(r) for display nconnection with the Merchant Impact Report herein described andillustrated in connection with FIGS. 1-3 f. According to one embodiment,the ROI system may be configured to determine a positive ROI marginbased upon past promotions offered and the margin thresholds necessaryfor a positive ROI in those past promotions. In another embodiment, theROI system may be configured to determine a positive ROI margin based atleast upon a Monte Carlo simulation used to derive an empiricaldistribution, from which the probability of π being positive may bedetermined, where π is the profit per instrument divided by the unitprice. The unit price may be defined as the price a consumer pays forthe goods, services, experiences and/or the like.

In this regard, π, the profits per instrument divided by the unit pricemay be expressed by the equation,

π=pf[(μ+s+r)−k(v+s+r)]−p(1−f)(v−μ)+f(1−p)[(p+s′+r′)−k(1+s′+r′)]−(1−p)(1−f)(1−μ)

wherein, v is the unit value divided by the unit price. The unit valuemay be defined as the original price of the good, service and/orexperience before the promotion was offered. Further,

-   -   1. r is the average return amount spent by a consumer divided by        the unit price (i.e. revenue associated with repeat business        (e.g., 214) per unit price),    -   2. r′ is the average return amount spent after the expiration of        the promotion period divided by the unit price (i.e., revenue        associated with repeat business after promotion expiration per        unit price),    -   3. s is the average amount spent in addition to the promotion        divided by the unit price (i.e., revenue upsale per unit price),    -   4. s′ is the average amount spent in addition to the promotion        after the promotion period expires divided by the unit price        (i.e., non-redemption revenue per unit price),    -   5. k is the variable cost as a percentage of the total check        amount (i.e., the variable cost as a percentage of the total        amount of a consumer's transaction),    -   6. f is the new customer fraction (i.e., the amount of new        consumers that had not previously purchased goods and/or        services from the provider), and    -   7. p is the final redemption percentage (i.e., the percentage of        instruments that are redeemed).        In addition, the above variables may be computed with respect to        the particular merchant's category.

In estimating the positive ROI margin, the ROI system may be configuredto utilize certain assumptions when performing the Monte Carlosimulations. For example, the ROI per instrument may be assumed to beindependent from the volume of the units sold. As such, when π, theprofits per instrument divided by the unit price, is greater than zero,the ROI may be interpreted as being positive for the promotion campaign.In some embodiments, the ROI system may assume a correlation existsbetween the unit price and whether a consumer purchases additional goodsand services and/or returns to the provider/merchant in the future forother goods, services and the like.

According to some embodiments, the provider parameter system may alsoassume that redeeming promotions by existing consumers is acannibalization of sales. Further, it may be assumed that a consumerwould spend the same amount regardless of having a promotion instrument,such as a coupon. The ROI system may further assume a final redemptionrate of 85%. In another embodiment, the provider parameter system mayuse a redemption rate percentage from the provider's past promotions.Further, it may be assumed that all expired instruments will be redeemedat the unit price. In some embodiments, the amount spent in addition tothe promotion and the amount spent in a subsequent visit by a consumermay be assumed to be zero for expired instruments.

As such, the positive ROI margin may be determined using theapproximated distributions obtained by the Monte Carlo simulations andconsumer input data corresponding to the category of the merchant c, thediscount provided d, and the cost of goods sold percentage k (e.g., 128,etc.), as represented by the equation,

${\mu_{r}( {m_{i},d} )} = \{ {{\mu \text{|}{P( {\pi( {\mu,c_{m_{i}},k_{c_{m_{i}}},\frac{1}{d}} )} )}} > 0} \}$

where the discount provided d is defined as 1/v. According to oneembodiment, the positive ROI margin may be determined to be 0.61 for aspa and health services provider, such as Acme Spa Company.

FIG. 3f shows one example cost editor interface displayed by the ROIsystem upon merchant selection of the cost editor 334. The cost editorinterface enables the merchant to select the percentage of the cost ofeach promotion that goes towards marginal costs of fulfilling thepromotion. In one such embodiment, food cost 128, discussed previously,corresponds to the marginal cost selected using cost editor 334. Inother embodiments, food cost 128 may only be one of many factors that gointo a merchant's calculation of the marginal cost of fulfilling apromotion. In yet other embodiments (e.g., non-food serving embodimentssuch as spas, etc.), food cost 128 may not be relevant to the marginalcost of fulfilling the promotion.

In the depicted embodiment, the merchant may interact with the costeditor interface by manipulating slider 336 until the appropriatepercentage is displayed. Because the marginal cost of fulfilling apromotion is highly dependent upon the merchant and the promotionoffered, slider 336 enables a merchant to calculate these costs in anymanner, and need not force the merchant to use a preconfigured formula.For any given percentage selected using slider 336, the cost editorinterface may display the value of the promotion 338 and, based on thepercentage selected using slider 336, the interface may display themarginal cost of fulfilling each promotion 340. Based on the valueselected using slider 336, the merchant will be returned to the revenueinterface 328, which will present an updated breakdown of the merchant'sfinancial information 332, as recalculated by the ROI system in view ofthe changed marginal cost. For example, in connection with FIG. 3e , theROI system may provide updated calculations for merchant cost 150 andmerchant profit 152 based on the newly edited cost information (e.g.,13%).

Using these additional Merchant Impact Report interface tools, the ROIsystem enables merchants to develop a much more sophisticatedunderstanding of the value provided by their promotions.

System Architecture

FIG. 4 shows a configuration 400 of the ROI system. The merchant 402 andsales representative 404 may calculate multiple ROI configurations andstore the ROI configurations for use (retrieval) in a promotionrepository 406 to build other promotions and/or use for comparison forconfiguring subsequent promotions. From the ROI sales representativeview, the sales representative may select, from previously calculatedpromotions, a default promotion for a merchant in order to initiate adialogue with the merchant. The ROI system may generate the merchantdeal page(s) (410) corresponding to the merchant's promotion that isviewable by the public in order to purchase the merchant's promotion.For example, potential customers may purchase the transaction via awebsite. In response to agreeing on the parameters of the transaction,the ROI system may generate a webpage for use on the website thatreflects the agreed parameters of the transaction. Further, the merchantand/or sales representative may review the webpage and make changes.Similar to the determination of the parameters for the transaction, themerchant (via the merchant computing device) and the salesrepresentative (via the website representative computing device) mayboth make changes to the webpage.

Return On Investment System Operations

FIGS. 5-7 show example operations for generating merchant revenue, cost,profit, and ROI information. The ROI information depends upon revenue,cost, and profit related to a promotion, values which themselves mayvary based on the several attributes assigned to the promotion and onprojections forecasting expected customer engagement as a result of thepromotion, as will be described below.

FIG. 5 shows a diagram of logic 500 of how merchant revenue iscalculated. In step 502, the system receives merchant criteriaselections. These selections may be received from the merchant or from asales representative of the promotion and marketing service whointeracts with the merchant. These selections comprise inputs indicativeof one or more attributes of the promotion, an upsell amount, and one ormore indicators of repeat business in response to the promotion. Theattributes may include the average check amount for two individuals, thevoucher price, the average voucher value, the number of customers pervoucher, a unit cap, a merchant share, a food cost percentage, a numberof impressions, and demographic information about the customers. Theindicators of repeat business may include return rate percentage andreturn visits per year. Using the received attributes, upsell amount,and indicators of repeat business, the merchant's revenue (shown as“Your Revenue” 130 in FIG. 1) is calculated based on revenue from“repeat customer revenue” 132, “additional spend revenue” 134 and “YourGroupon Check” 136 amount calculated based on the received criteria(e.g., merchant selected criteria).

In operation 504, the ROI system calculates, based on one or more of theattributes received in operation 502, a first amount indicative ofrevenue generated from the promotion. In one embodiment, the repeatcustomer revenue is calculated using the following formula (aspreviously described):

${{Repeat}\mspace{14mu} {Customer}\mspace{14mu} {Revenue}} = {{Unit}\mspace{14mu} {Cap} \times \frac{Customers}{Groupon} \times {Average}\mspace{14mu} {Check}\mspace{14mu} {for}\mspace{14mu} 2 \times ( {{Return}\mspace{14mu} {Rate}\mspace{14mu} \%} ) \times \frac{{Return}\mspace{14mu} {Visits}}{Year}}$

In operation 506, the ROI system calculates, based on the upsell amountreceived in operation 502, a second amount indicative of revenuegenerated from promotion upsells. This second amount may compriserevenue generated from upsells attendant to administering the promotion.In one embodiment, this second amount is calculated using the followingformula (as previously described):

Additional Spend Revenue=Unit Cap×Average Upsell

In operation 508, the ROI system calculates, based on the one or moreindicators of repeat business received in operation 502, a third amountindicative of revenue generated from repeat business attendant toadministering the promotion. In one embodiment, the merchant's checkrevenue 136 is calculated using the following formula (as previouslydescribed):

Your Groupon Check=Unit Cap×Merchant Share

In operation 510, the merchant's revenue is determined from the first,second, and third amounts.

Subsequently, the ROI system calculates, based on the one or moreattributes of the promotion, a fourth amount indicative of costs fromthe promotion. In this regard, FIG. 6 shows a flow diagram of logic 600of how this cost is calculated. In operation 602, the ROI systemreceives the merchant criteria selections. The merchant's total costincludes the repeat revenue cost, the additional spend cost, and themerchant's check cost. The costs may be calculated by multiplying thecorresponding revenue by the received food cost, as describedpreviously. Accordingly, in operation 604, the ROI system calculates therepeat revenue cost. In one embodiment, this calculation comprisesmultiplying the repeat customer revenue 132 by the Food Cost 128. Inoperation 606, the ROI system calculates the additional spend cost. Inone such embodiment, this calculation comprises multiplying theadditional spend revenue 132 by the Food Cost 128. In operation 608, theROI system calculates the merchant's check cost 144, which in oneembodiment comprises multiplying the Your Groupon Check revenue 136 bythe Average Groupon Value 108 and the Food Cost 128. Accordingly, inoperation 610, the ROI system determines the merchant's total cost byadding together the repeat revenue cost, the additional spend cost, andthe merchant's check cost.

FIG. 7 shows a flow diagram 700 describing an example mechanism by whichmerchant profit is calculated. In operation 702, the ROI system receivesthe merchant criteria selections. In operation 705, the ROI systemcalculates the merchant revenue. In one embodiment, the merchant revenueis calculated as shown above in operation 510. Subsequently, inoperation 706, the ROI system calculates the merchant cost. In oneembodiment, the merchant cost is calculated as shown above in operation610. Finally, in operation 708, the ROI system determines the merchantprofit. In this regard, the merchant's profit comprises the merchantrevenue minus the merchant cost.

In some embodiments, the ROI system subsequently generates (or updates)a graphical representation displaying the first, second, third, andfourth amounts. In one such embodiment, the graphical representationcomprises a first histogram representative of the first, second, andthird amounts, and a second histogram representative of the fourthamount. Examples of such graphical representations may be found in FIGS.1-3 b. The graphical representation may provide a forecast using apredictive wizard (i.e., software that automatically calculates outcomesbased on various inputs), analytics/demographics (e.g., historicalinformation), similar promotions, or any combination thereof. In somecases, this forecast may include expected profit, an expected number ofnew customers, an indication of the investment spent per new customer,or an indication of a ratio showing an expected return on investment(shown, for example, in FIG. 1, elements 152, 156, 158, and 160,respectively).

In some embodiments, the graphical representation may be a graphicalconsumer interface (GUI) with which the merchant and salesrepresentative may provide the inputs used in the above-describedcalculations and forecasts. In such embodiments, the ROI system mayreceive input from the sales representative to lock or unlock certainfields in the interface and may allow off-line merchant manipulation ofthe graphical representation. Accordingly, the unified payment and ROIsystem may generate a real-time ROI as output for one or morepromotions.

ROI Learning Model

In some example embodiments, attribute analysis for predicting ROI orROI components includes a pattern recognition algorithm for processinghistoric metrics to determine a given providers ROI based on theproviders attributes. Cluster analysis and classification algorithms aretwo examples of pattern recognition algorithms that may be used toperform processing using statistical inference. In cluster analysis, aninput pattern is assigned to one of several groups (clusters) of thesame type of patterns. Patterns within the same cluster are likely to bemore similar to each other than they are similar to patterns assigned todifferent clusters. A classification algorithm (i.e. classifier) maps aninput pattern into one of several categories in which the pattern ismost likely to belong.

Machine learning is often used to develop a particular patternrecognition algorithm (i.e. an algorithm that represents a particularpattern recognition problem) that is based on statistical inference. Forexample, a set of clusters may be developed using unsupervised learning,in which the number and respective sizes of the clusters is based oncalculations of similarity of features of the patterns within apreviously collected training set of patterns. In another example, aclassifier representing a particular categorization problem may bedeveloped using supervised learning based on using a training set ofpatterns and their respective known categorizations. Each trainingpattern is input to the classifier, and the difference between theoutput categorization generated by the classifier and the knowncategorization is used to adjust the classifier coefficients to moreaccurately represent the problem. A classifier that is developed usingsupervised learning also is known as a trainable classifier.

In embodiments, content analysis includes a source-specific classifierthat takes a source-specific representation of the content received froma particular source as an input and produces an output that categorizesthe provider attributes in such a way to predict certain metric valuesthat can be used to calculate an ROI or ROI component.

FIG. 7a shows an example method that may be executed by a unifiedpayment and ROI system to train and execute an ROI prediction model thatis configured to predict ROI and ROI components based on providerattributes and one or more historical metrics. As is shown in block 710,ROI and/or ROI components are calculated for one or more providers. Insome example, embodiments, the ROI and/or ROI components may becalculated based on one or more metrics generated from surveys,marketing exposure, financial engineering, in-store transactions and/orthe like. The metrics may include but are not limited to:

ID Metric Description 1. Delta In some examples, Delta is the timeperiod used to define new, lapsed, existing, and returning transactions.This is an input assumption and the default may take the form of delta =90 days. Given a consumer and a transaction by that consumer, thetransaction is considered new/lapsed if there are no transactions by thesame consumer in the preceding delta days, and vice versa forexisting/returning. A lapsed transaction, for example, has notransactions by the same consumer in the preceding delta days, but thereare transactions by the same consumer more than delta days ago. Notethat if a transaction is less than delta days from the min_tran_date(see below), then the status of the transaction as new/lapsed orreturning is undefined. 2. final_ The fraction of Groupons redeemed atthe time redemption_pct of campaign expiration. This is an inputassumption and the default may take the form of 0.85. 3. cc_fee_pctCredit card fee percentage. There is a fee charged by the credit cardcompany when customers purchase Groupons, in some examples, using theircredit card on the Groupon website. This fee is a percentage of thetransaction amount and it comes out of the provider's share of theGroupon sales. This is an input assumption and the default may take theform of 0.025. 4. alpha Significance level of the confidence intervals.The confidence level is 1-alpha. This is an input assumption and thedefault may take the form of 0.05 (95% confidence). 5. cog_pct Cost ofgoods or services as a percentage of the total bill. This inputassumption is tabulated by provider category and subcategory. E.g. forproviders in category Restaurants and subcategory American/Traditional,the cog_pct is 0.37. In this case, the provider cost for a total bill of$100 (excluding tips but including tax) is assumed to be $37. 6.tips_pct Tips as a percentage of the total bill. This is currentlyassumed to be 20% for providers in categories Restaurants and Beauty &Spas and 0% otherwise. This is used, in some examples, to back out theestimated total bill, excluding tip, from the actual transaction amount.7. min_tran_date The date of the first transaction in the data for aprovider, expressed as a number. Use the mysql function from_days( ) toget the actual date. 8. max_tran_date The date of the last transactionin the data for a provider, expressed as a number. Use the mysqlfunction from_days( ) to get the actual date. 9. num_txn The number oftotal transactions in the data for a provider. 10. num_ Number ofGroupons sold. coupons_sold 11. num_cstmr Number of unique customers whopurchased Groupons. 12. num_coupons_ Number of Groupons redeemedto-date. redeemed_td 13. num_coupons_ Projected number of Grouponsredeemed at redeemed_proj campaign expiration. 14. num_cstmr_ Number ofunique customers who redeemed redeemed_td Groupons to-date. 15.num_cstmr_ Projected number of unique customers who redeemed_projredeemed Groupon at campaign expiration. 16. num_email_ Number of emailimpressions. impressions 17. unit_value_avg The average Groupon unitvalue weighted by the number sold. 18. unit_price_avg The averageGroupon unit price weighted by the number sold. 19. unit_buy_ Theaverage Groupon unit buy price weighted price_avg by the number sold.20. num_new_cstmr Number of new/lapsed customers activated by Groupon.This number reflects only those customers who appear in our transactiondata. Generally, these are customers who have used theirGroupon-registered credit cards at the provider. More specifically, andby way of example, new customers are those who: 1 Bought a Groupon 2 Hadan in-store transaction using a  registered credit card after Groupon purchase 3 Have not visited the store in the delta days  before thein-store transaction 4 If visit is by a lapsed customer, there are  novisits between the visit and the Groupon  purchase date 21.num_existing_ Number of existing customers who use cstmr Groupon. Seenum_new_cstmr above for important example caveats. Existing customersare those who: 1 Bought a Groupon 2 Had an in-store transaction using a registered credit card after Groupon  purchase 3 Had visited the storein the delta days  before the in-store transaction 4 The previous visitmust be before the  Groupon purchase date 22. new_cstmr_ The customerfraction is defined as, in some fraction examples:  new cstmr fraction =num new cstmrnum   new cstmr + num existing cstmr This metric is used toestimate the new customer fraction of all Groupon customers of theprovider, even though it is calculated based only on the subpopulationof consumers whose transactions we are able to track. 23. existing_ Theexisting customer fraction is defined as, in cstmr_fraction someexamples:   existing cstmr fraction = num existing  cstmrnum new cstmr +num existing cstmr Similar to new_cstmr_fraction, this metric is used toestimate the existing customer fraction of all Groupon customers of theprovider. 24. new_cstmr_ The new customer fraction based on emailfraction_survey survey data. The survey new customers are those whoindicated that they have never been to the provider before. Note thatnew_cstmr_fraction_survey does not measure the same thing asnew_cstmr_fraction in some examples, as new_cstmr_fraction include alsolapsed customers. 25. lapsed_cstmr_ The lapsed customer fraction basedon email fraction_survey survey data. The survey lapsed customers arethose, in some examples, who indicated that they have visited the storebefore, but more than 90 days ago. 26. new_lapsed_ The new and lapsedcustomer fraction based on cstmr_fraction_ email survey data. Thismetric is analogous to survey new_cstmr_fraction in examples where delta= 90. 27. existing_cstmr_ The existing customer fraction based on emailfraction_survey survey data. The survey existing customers are those, insome examples, who indicated that they have visited the store in thepast 90 days. This metric is analogous to existing_cstmr_fraction, butonly if delta = 90. 28. new_cstmr_ This is either new_cstmr_fraction orfraction_best new_lapsed_cstmr_fraction_survey. The rule to determinewhich metric to use is described, for example, in overspend_avg_best.This is the customer fraction used in subsequent calculations. 29.existing_cstmr_ Existing customer fraction defined, for fraction_bestexample, as 1-new_cstmr_fraction_best. This is the customer fractionused in subsequent calculations. 30. overspend_avg The average overspendestimated from data entered by the provider. The data is, for example,the amount_spent column in the campaign_membership_coupons table ingroupon_production. It is assumed, in some examples, that the value inthe amount_spent columns is the total bill including the Groupon valuebut excluding tips. 31. matched_ The average overspend estimated fromredemption_ transactions matched to redemptions. overspend_avg 32.overspend_ The fraction of consumers who overspend frac_cat_avg averagedby provider category. This quantity is, for example, measured from dataentered by providers. 33. overspend_ Estimated average overspend iscalculated for avg_est example by: overspend_avg_est =matched_redemption_overspend_avg * overspend_frac_cat_avg 34. overspend_A “best estimate” of the average overspend and avg_best is, in someexamples, either overspend_avg or overspend_avg_est. This metric is usedin the calculation of other metrics, such as the overspend revenue. Therules to determine which one to use, in some examples, are as follows: 1If overspend_avg_est cannot be computed  (usually because there are nomatched  redemptions), use overspend_avg 2 Else if overspend_avg cannotbe  computed (usually because the provider  did not track this number),or if  overspend_avg/SE(overspend_avg) < 1  and overspend_avg_est/SE(overspend_avg_es  t) > 1, then useoverspend_avg_est. Here  SE(x) is the standard error of x. 3 Else, useoverspend_avg. 35. ret_fraction_new Fraction of new/lapsed customers whoreturn. 36. ret_freq_new Return frequency of new/lapsed Grouponcustomers. The return frequency is computed, for example, using amaximum likelihood estimate with the assumption that the number ofreturn visits by a customer follows a Poisson distribution. 37.ret_freq_new_ Return frequency of new/lapsed Groupon ret_only customerswith observed return visits. 38. avg_spend_ Average spend of new/lapsedGroupon ret_new customers on return visits. 39. ret_fraction_ Returnfraction of all new/lapsed customers, all_new both Groupon andnon-Groupon. 40. ret_freq_all_new Return frequency of all new/lapsedcustomers. 41. ret_freq_all_ Return frequency of all new/lapsedcustomers new_ret_only with observed return visits. 42. avg_spend_Average spend of all new/lapsed customers on ret_all_new return visits.43. ret_fraction_ Estimated example return fraction of new_estnew/lapsed Groupon customers. For example, can be defined as 0.7 *ret_fraction_all_new. 44. ret_freq_new_est Estimated example returnfrequency of new/lapsed Groupon customers. Currently defined, forexample, as 1.0 * ret_freq_all_new. 45. ret_freq_new_ Estimated returnfrequency of new/lapsed ret_only_est Groupon customers with observedreturn visits. Currently defined, for example, as 0.46 *ret_freq_all_new_ret_only. 46. avg_spend_ Estimated average spend ofnew/lapsed ret_new_est Groupon customers on return visits. Currentlydefined, for example, as 0.8 * avg_spend_ret_all_new. 47. ret_fraction_A “best estimate” return fraction of new/lapsed new_best Grouponcustomers. Is it either ret_fraction_new or ret_fraction_new_est. Therules to determine which version to use are the Same as, in someexamples, overspend_avg_best. 48. ret_freq_new_ Same as, in someexamples, ret_only_best ret_fraction_new_best but for return frequencyof customers with observed return visits. 49. avg_spend_ Same as, insome examples, ret_new_best ret_fraction_new_best but for return averagespend. 50. num_ret_ Number of return visits to-date by new/lapsedvst_td_new Groupon customers. Defined as, in some examples:  num_ret_vst_td_new =   num_cstmr_redeemed_td *   new_cstmr_fraction *  ret_fraction_new_best *   ret_freq_new_best * nday/365 Here nday isthe average number of days since redemption averaged over all Grouponcustomers. 51. num_ret_ Number of return customers to-date bycstmr_td_new new/lapsed Groupon customers. Defined as, for example:  num_ret_cstmr_td_new =   num_cstmr_redeemed_td *  new_cstmr_fraction * ret_fraction_new_best 52. num_email_ Number ofemail marketing visits to-date. mkt_vst_td 53. num_email_ Number ofemail marketing visits projected. mkt_vst_proj 54. avg_spend_ Averagespend on email marketing visits. email_mk 55. rev_groupon_ Total revenuefrom Groupon campaigns. campaign Estimated based on the number of“collected” coupons and the unit_buy_price with a correction for thecredit card transaction fee. This may, in some examples, be differentfrom the actual amount paid to the provider by Groupon. 56.rev_email_mkt_td Revenue to-date from email marketing visits. 57.rev_email_ Projected revenue from email marketing visits. mkt_proj 58.rev_overspend_td Overspend revenue to-date from all Groupon customers.Defined as, in some examples: rev_overspend_td =num_coupons_redeemed_td * overspend_avg_best 59. rev_ This is the sum ofthe total overspend revenue overspend_proj from expired campaigns andthe projected overspend revenue from active campaigns. The overspendrevenue of expired campaigns is computed the same way as overspendrevenue to-date. The projected overspend revenue is (number of projectedredemptions) * (overspend_avg_best). 60. rev_overspend_  Overspendrevenue to-date from td_new  new/lapsed Groupon customers. Defined  as,in some examples: rev_overspend_td_new = rev_overspend_td *new_cstmr_fraction 61. rev_overspend_  Projected Overspend revenue fromproj_new  new/lapsed Groupon customers. Defined  as, in some examples:rev_overspend_proj_new = rev_overspend_proj * new_cstmr_fraction 62.rev_ret_td_new Return visits revenue to-date from new/lapsed Grouponcustomers. Defined as, in some examples: rev_ret_td_new =num_ret_vst_td_new * avg_spend_ret_new_best 63. rev_ret_proj_newProjected Return visits revenue from new/lapsed Groupon customers.Defined as, in some examples:   rev_ret_proj_new         =     num_coupons_redeemed_proj *   ret_fraction_new_best *     ret_freq_new_ret_only_best *   avg_spend_ret_new_best *  new_cstmr_fraction 64. rev_tot_td Total revenue to-date. Defined as,in some examples:   rev_tot_td = rev_groupon_campaign +  rev_overspend_td + rev_ret_td_new +   rev_email_mkt_td 65.rev_tot_proj Projected total revenue. Defined as, in some examples:  rev_tot_proj = rev_groupon_campaign +   rev_overspend_proj +  rev_ret_proj_new +   rev_email_mkt_proj 66. cog_email_mkt_td Cost ofgoods to-date of email marketing visits. 67. cog_email_cog_email_mkt_proj mkt_proj 68. cog_  Groupon redemption cost of goodsto-date. redemptions_td  Defined as, in some examples:  cog_redemptions_td =      num_coupons_redeemed_td *    cog_pct *(unit_value_avg +    overspend_avg_best) 69. cog_ Projected Grouponredemption cost of goods redemptions_proj to-date. Defined as, in someexamples: cog_redemptions_proj =   num_coupons_redeemed_proj * cog_pct *(unit_value_avg + overspend_avg_best) 70. cog_redemptions_  Grouponredemption cost of goods to-date td_new  of new/lapsed customers.Defined as, in  some examples: cog_redemptions_td_new =  cog_redemptions_td * new_cstmr_fraction 71. cog_redemptions_ ProjectedGroupon redemption cost of goods proj_new of new/lapsed customers.Defined as, in some examples: cog_redemptions_proj_new =cog_redemptions_proj * new_cstmr_fraction 72. cog_discount_ Discountprovided to existing customers in provided_ Groupon campaign to-date.Defined as, in td_existing some examples:cog_discount_provided_td_existing = existing_cstmr_fraction *num_coupons_redeemed_td * [unit_value_avg-(unit_buy_price_avg-unit_price_avg * cc_fee_pct)] 73. cog_discount_Discount provided to existing customers in provided_ Groupon campaignprojected. Defined as, in proj_existing some examples:  cog_discount_provided_td_existing = existing_cstmr_fraction *num_coupons_redeemed_proj * [unit_value_avg-(unit_buy_price_avg-unit_price_avg * cc_fee_pct)] 74. cog_ret_td_new Cost of goodsassociated with return visits to- date by new/lapsed Groupon customers.Defined as, in some examples: cog_ret_td_new = rev_ret_td_new * cog_pct75. cog_ret_proj_new  Cost of goods (projected) associated with  returnvisits by new/lapsed Groupon  customers. Defined as, in some examples:cog_ret_proj_new = rev_ret_proj_new * cog_pct 76. cog_tot_td Total costof goods to-date. Defined as, in some examples: cog_tot_td =cog_redemptions_td + cog_ret_td_new 77. cog_tot_proj Total cost of goodsprojected. Defined as, in some examples: cog_tot_proj =cog_redemptions_proj + cog_ret_proj_new 78. provider_profit_td Totalprovider profit to-date. Defined as, in some examples:provider_profit_td = rev_tot_td-cog_tot_td 79. provider_ Total providerprofit projected. Defined as, in profit_proj some examples:provider_profit_proj = rev_tot_proj- cog_tot_proj 80. tax_pct Sales taxpercentage 81. num_email_ Number of email impressions in the featureimpressions_ position featured 82. invoiced_ Total invoiced payments forthe Groupon groupon_ campaign campaign 83. payments_ Total payments forthe Groupon campaign groupon_campaign 84. payments_ Eitherrev_groupon_campaign or groupon_ payments_groupon_campaign depending oncampaign_best the payments data source 85. invoiced_groupon_ Eitherrev_groupon_campaign or campaign_best invoiced_groupon_campaigndepending on the payments data source

As is shown in block 712, an ROI prediction model may be trained usinghistorical calculated ROI, ROI components and related metrics. In someexample embodiments, the ROI prediction model may take the form of asupport vector machine, decision tree learning, association rulelearning, artificial neural networking, inductive logic programming,clustering, and/or the like.

Using the previously computed metrics in block 710 (and related ROI andROI components) a dataset may be generated that is configured to trainand/or test the ROI prediction model to approximate ROI and/or ROIcomponents based on certain provider attributes. In some exampleembodiments, the training may include classifying metrics, providerattributes or the like as effecting or otherwise informative of an ROIvalue or a particular ROI component. In other example embodiments, theROI prediction model may additionally or alternatively be trained sothat it may estimate a value of a particular metric for a provider basedon that providers stated attributes. As such, the trained ROI predictionmodel may provide a predictive algorithm that is operable to predict ROIand/or ROI components using input provider attributes.

As is shown in block 714, one or more attributes may be received for agiven provider. In some examples, the given provider may take the formof a current provider that is running a promotion, a provider interestedin a running a promotion and/or the like. As such, in order to provide apredicted ROI and/or ROI components, the provider may provide attributessuch as, size, category, location, past deal history, sales data,advertising data, current metrics and/or the like.

As is shown in block 716, a predicted ROI and/or predicted ROIcomponents (such as is shown with reference to FIG. 3a ) may becalculated for the provider based on the attributes and the ROIprediction model. In some example embodiments, the one or moreattributes may be provided to the ROI prediction model. The ROIprediction model then may compare the receive attributes to its traineddataset, the comparison generating a prediction of ROI for the givenprovider based on ROI metrics for historical providers having similarattributes. The predicted ROI and/or predicated ROI components may thenbe provided for display via the impact report, such as is shown withreference to FIG. 3 e.

System Components

FIG. 8 shows configuration 800 of the unified payment and ROI system802. The unified payment system 802 may be deployed as a generalcomputer system used in a networked deployment. The unified paymentsystem 802 may represent a remote server or local mobile device of theconsumer. In other words, the unified payment logic may be executed byone or more processors locally or remotely located. The computer systemmay operate as a server or as a client consumer computer in aserver-client consumer network environment, or as a peer computer systemin a peer-to-peer (or distributed) network environment. The computersystem may also be implemented as or incorporated into various devices,such as a personal computer (PC), a tablet PC, a set-top box (STB), apersonal digital assistant (PDA), a mobile device, a palmtop computer, alaptop computer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any other machinecapable of executing a set of instructions 810 (sequential or otherwise)that specify actions to be taken by that machine. In a particularembodiment, the computer system may be implemented using electronicdevices that provide voice, video or data communication. Further, whilea single computer system may be illustrated, the term “system” shallalso be taken to include any collection of systems or sub-systems thatindividually or jointly execute a set, or multiple sets, of instructionsto perform one or more computer functions.

The computer system may include a processor 803, such as, a centralprocessing unit (CPU), a graphics processing unit (GPU), or both. Theprocessor may be a component in a variety of systems. For example, theprocessor may be part of a standard personal computer or a workstation.The processor may be one or more general processors, digital signalprocessors, application specific integrated circuits, field programmablegate arrays, servers, networks, digital circuits, analog circuits,combinations thereof, or other now known or later developed devices foranalyzing and processing data. The processors and memories 804 discussedherein, as well as the claims below, may be embodied in and implementedin one or multiple physical chips or circuit combinations. The processor803 may execute a software program 810, such as code generated manually(i.e., programmed).

The computer system 802 may include a memory 804 that can communicatevia a bus. The memory 804 may be a main memory, a static memory, or adynamic memory. The memory 804 may include, but may not be limited tocomputer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In onecase, the memory 804 may include a cache or random access memory for theprocessor. Alternatively or in addition, the memory 804 may be separatefrom the processor, such as a cache memory of a processor, the memory,or other memory. The memory 804 may be an external storage device ordatabase for storing data. Examples may include a hard drive, compactdisc (“CD”), digital video disc (“DVD”), memory card, memory stick,floppy disc, universal serial bus (“USB”) memory device, or any otherdevice operative to store data. The memory 804 may be operable to storeinstructions executable by the processor. The functions, acts or tasksillustrated in the figures or described herein may be performed by theprogrammed processor executing the instructions stored in the memory.The functions, acts or tasks may be independent of the particular typeof instructions set, storage media, processor or processing strategy andmay be performed by software, hardware, integrated circuits, firm-ware,micro-code and the like, operating alone or in combination. Likewise,processing strategies may include multiprocessing, multitasking,parallel processing and the like.

The computer system may further include a display 812, such as a liquidcrystal display (LCD), an organic light emitting diode (OLED), a flatpanel display, a solid state display, a cathode ray tube (CRT), aprojector, a printer or other now known or later developed displaydevice for outputting determined information. The display 812 may act asan interface for the consumer to see the functioning of the processor,or specifically as an interface with the software stored in the memoryor in the drive unit.

Additionally, the computer system may include an input device 814configured to allow a consumer to interact with any of the components ofsystem. The input device may be a number pad, a keyboard, or a cursorcontrol device, such as a mouse, or a joystick, touch screen display,remote control or any other device operative to interact with thesystem.

The computer system may also include a disk or optical drive unit. Thedisk drive unit 808 may include a computer-readable medium 806 in whichone or more sets of instructions, e.g. software, can be embedded.Further, the instructions may perform one or more of the methods orlogic as described herein. The instructions may reside completely, or atleast partially, within the memory 804 and/or within the processorduring execution by the computer system. The memory 804 and theprocessor also may include computer-readable media as discussed above.

The present disclosure contemplates a computer-readable medium 806 thatincludes instructions or receives and executes instructions responsiveto a propagated signal, so that a device connected to a network 816 maycommunicate voice, video, audio, images or any other data over thenetwork 816. Further, the instructions may be transmitted or receivedover the network 816 via a communication interface 818. Thecommunication interface may be a part of the processor or may be aseparate component. The communication interface may be created insoftware or may be a physical connection in hardware. The communicationinterface may be configured to connect with a network, external media,the display, or any other components in system, or combinations thereof.The connection with the network may be a physical connection, such as awired Ethernet connection or may be established wirelessly as discussedbelow. Likewise, the additional connections with other components of thesystem 802 may be physical connections or may be established wirelessly.In the case of a service provider server, the service provider servermay communicate with consumers through the communication interface.

The network may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols.

The computer-readable medium 806 may be a single medium, or thecomputer-readable medium 806 may be a single medium or multiple media,such as a centralized or distributed database, and/or associated cachesand servers that store one or more sets of instructions. The term“computer-readable medium” may also include any medium that may becapable of storing, encoding or carrying a set of instructions forexecution by a processor or that may cause a computer system to performany one or more of the methods or operations disclosed herein.

The computer-readable medium 806 may include a solid-state memory suchas a memory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium 806 also may be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium 806 may include a magneto-optical oroptical medium, such as a disk or tapes or other storage device tocapture carrier wave signals such as a signal communicated over atransmission medium. A digital file attachment to an email or otherself-contained information archive or set of archives may be considereda distribution medium that may be a tangible storage medium. Thecomputer-readable medium 806 may comprise a tangible storage medium. Thecomputer-readable medium 806 may comprise a non-transitory medium inthat it cannot be construed to refer to carrier signals or propagatingwaves. Accordingly, the disclosure may be considered to include any oneor more of a computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored.

Alternatively or in addition, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments may broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that may be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system may encompass software, firmware, and hardwareimplementations.

The methods described herein may be implemented by software programsexecutable by a computer system. Further, implementations may includedistributed processing, component/object distributed processing, andparallel processing. Alternatively or in addition, virtual computersystem processing maybe constructed to implement one or more of themethods or functionality as described herein.

Although components and functions are described that may be implementedin particular embodiments with reference to particular standards andprotocols, the components and functions are not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, andHTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations described herein are intended to provide a generalunderstanding of the structure of various embodiments. The illustrationsare not intended to serve as a complete description of all of theelements and features of apparatus, processors, and systems that utilizethe structures or methods described herein. Many other embodiments maybe apparent to those of skill in the art upon reviewing the disclosure.Other embodiments may be utilized and derived from the disclosure, suchthat structural and logical substitutions and changes may be madewithout departing from the scope of the disclosure. Additionally, theillustrations are merely representational and may not be drawn to scale.Certain proportions within the illustrations may be exaggerated, whileother proportions may be minimized Accordingly, the disclosure and thefigures are to be regarded as illustrative rather than restrictive.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the description. Thus, to the maximumextent allowed by law, the scope is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

1-78. (canceled)
 79. A method for utilizing at least a processor, amemory, and a display device for rendering a graphical user interface(GUI) comprising: receiving, via the GUI, an input indicative of one ormore metrics relating to a merchant and a promotion; generating, by theprocessor, a return on investment (ROI) learning model based on the oneor more metrics relating to the merchant and the promotion; updating, bythe processor, the ROI learning model by automatically inputting asecond one or more metrics relating to the merchant and the promotion,wherein the second one or more metrics relating to the merchant and thepromotion are determined based on at least one or more historicalmetrics retrieved from a promotion repository; generating, by theprocessor, a repeat revenue visual metric for the promotion, wherein therepeat business revenue visual metric is indicative of revenue predictedto be generated from one or more repeat business transactions associatedwith the promotion based on the updated ROI learning model; generating,by the processor, a merchant impact report, comprising the repeatbusiness revenue visual metric; and displaying, via the GUI, themerchant impact report.
 80. The method of claim 79, further comprising:generating, via the processor, a set of clusters based on one or moresimilar return ROI learning models previously generated.
 81. The methodof claim 79, further comprising: determining a source-specificclassifier; inputting the one or more metrics relating to the merchantand the promotion into the source-specific classifier; inputting thesecond one or more metrics relating to the merchant and the promotioninto the source-specific classifier; and determining, via the processor,an ROI or an ROI component.
 82. The method of claim 79, wherein the oneor more metrics or the second one or more metrics are generated based atleast in part on one or more of a survey, a marketing exposure, afinancial engineering transaction, an in-store transaction, or a webpagetransaction.
 83. The method of claim 79, wherein the merchant impactreport comprises one or more amounts indicative of a revenue from thepromotion or a cost of the promotion, wherein the one or more amountsare determined based on the one or more metrics relating to the merchantand the promotion.
 84. The method of claim 79, wherein the return oninvestment (ROI) learning model is a support vector machine, decisiontree learning, association rule learning, artificial neural networking,inductive logic programming, or clustering.
 85. The method of claim 79,further comprising: generating, via the processor, a dataset based onthe one or more historical metrics; comparing, via the processor, thereturn on investment (ROI) learning model to the dataset; training, viathe processor, the return on investment (ROI) learning model to classifya particular metric value, determined by a combination of metrics, asindicative of a positive ROI; and storing, via the processor and thememory, the combination of metrics used as a predictor for a number ofconsumers who will return to the merchant after a first visit using thepromotion.
 86. An apparatus comprising: a processor; a memory includingcomputer program code; and a display device for rendering a graphicaluser interface (GUI), the memory and the computer program codeconfigured to, with the processor, cause the apparatus to at least:receive, via the GUI, an input indicative of one or more metricsrelating to a merchant and a promotion; generate, by the processor, areturn on investment (ROI) learning model based on the one or moremetrics relating to the merchant and the promotion; update, by theprocessor, the ROI learning model by automatically inputting a secondone or more metrics relating to the merchant and the promotion, whereinthe second one or more metrics relating to the merchant and thepromotion are determined based on at least one or more historicalmetrics retrieved from a promotion repository; generate, by theprocessor, a repeat revenue visual metric for the promotion, wherein therepeat business revenue visual metric is indicative of revenue predictedto be generated from one or more repeat business transactions associatedwith the promotion based on the updated ROI learning model; generate, bythe processor, a merchant impact report, comprising the repeat businessrevenue visual metric; and display, via the GUI, the merchant impactreport.
 87. The apparatus of claim 86, wherein the at least one memoryincluding the computer program code is further configured to, with theat least one processor, cause the apparatus to: generate, via theprocessor, a set of clusters based on one or more similar return ROIlearning models previously generated.
 88. The apparatus of claim 86,wherein the at least one memory including the computer program code isfurther configured to, with the at least one processor, cause theapparatus to: determine a source-specific classifier; input the one ormore metrics relating to the merchant and the promotion into thesource-specific classifier; input the second one or more metricsrelating to the merchant and the promotion into the source-specificclassifier; and determine, via the processor, an ROI or an ROIcomponent.
 89. The apparatus of claim 86, wherein the one or moremetrics or the second one or more metrics are generated based at leastin part on one or more of a survey, a marketing exposure, a financialengineering transaction, an in-store transaction, or a webpagetransaction.
 90. The apparatus of claim 86, wherein the merchant impactreport comprises one or more amounts indicative of a revenue from thepromotion or a cost of the promotion, wherein the one or more amountsare determined based on the one or more metrics relating to the merchantand the promotion.
 91. The apparatus of claim 86, wherein the return oninvestment (ROI) learning model is a support vector machine, decisiontree learning, association rule learning, artificial neural networking,inductive logic programming, or clustering.
 92. The apparatus of claim86, wherein the at least one memory including the computer program codeis further configured to, with the at least one processor, cause theapparatus to: generate, via the processor, a dataset based on the one ormore historical metrics; compare, via the processor, the return oninvestment (ROI) learning model to the dataset; train, via theprocessor, the return on investment (ROI) learning model to classify aparticular metric value, determined by a combination of metrics, asindicative of a positive ROI; and store, via the processor and thememory, the combination of metrics used as a predictor for a number ofconsumers who will return to the merchant after a first visit using thepromotion.
 93. A computer program product comprising: at least onecomputer readable non-transitory memory medium having program codeinstructions stored thereon, the program code instructions which whenexecuted by an apparatus, comprising a processor, a memory, and adisplay device for rendering a graphical user interface (GUI), cause theapparatus at least to: receive, via the GUI, an input indicative of oneor more metrics relating to a merchant and a promotion; generate, by theprocessor, a return on investment (ROI) learning model based on the oneor more metrics relating to the merchant and the promotion; update, bythe processor, the ROI learning model by automatically inputting asecond one or more metrics relating to the merchant and the promotion,wherein the second one or more metrics relating to the merchant and thepromotion are determined based on at least one or more historicalmetrics retrieved from a promotion repository; generate, by theprocessor, a repeat revenue visual metric for the promotion, wherein therepeat business revenue visual metric is indicative of revenue predictedto be generated from one or more repeat business transactions associatedwith the promotion based on the updated ROI learning model; generate, bythe processor, a merchant impact report, comprising the repeat businessrevenue visual metric; and display, via the GUI, the merchant impactreport.
 94. The computer program product of claim 93, further comprisingprogram code instructions, the program code instructions which whenexecuted by the apparatus further cause the apparatus at least to:generate, via the processor, a set of clusters based on one or moresimilar return ROI learning models previously generated.
 95. Thecomputer program product of claim 93, further comprising program codeinstructions, the program code instructions which when executed by theapparatus further cause the apparatus at least to: determine asource-specific classifier; input the one or more metrics relating tothe merchant and the promotion into the source-specific classifier;input the second one or more metrics relating to the merchant and thepromotion into the source-specific classifier; and determine, via theprocessor, an ROI or an ROI component.
 96. The computer program productof claim 93, wherein the one or more metrics or the second one or moremetrics are generated based at least in part on one or more of a survey,a marketing exposure, a financial engineering transaction, an in-storetransaction, or a webpage transaction.
 97. The computer program productof claim 93, wherein the merchant impact report comprises one or moreamounts indicative of a revenue from the promotion or a cost of thepromotion, wherein the one or more amounts are determined based on theone or more metrics relating to the merchant and the promotion.
 98. Thecomputer program product of claim 93, further comprising program codeinstructions, the program code instructions which when executed by theapparatus further cause the apparatus at least to: calculate, based onthe upsell amount, a third amount indicative of revenue generated frompromotion upsells; present the merchant impact report including thefirst amount, the second amount, and the third amount; generate, via theprocessor, a dataset based on the one or more historical metrics;compare, via the processor, the return on investment (ROI) learningmodel to the dataset; train, via the processor, the return on investment(ROI) learning model to classify a particular metric value, determinedby a combination of metrics, as indicative of a positive ROI; and store,via the processor and the memory, the combination of metrics used as apredictor for a number of consumers who will return to the merchantafter a first visit using the promotion.